Big Data Study for Gluten-Free Foods in India and USA Using Online Reviews and Social Media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Celiac disease, gluten-allergy or gluten-sensitivity is caused due to glutamine protein from the grains like wheat, rye and barley (collectively called gluten). This protein damages the small intestine and causes stomach pain, bloating, weakness etc. Celiac disease, gluten-allergy or gluten-sensitivity has never really been taken seriously in developing countries like India. However, in developed nations like UK, USA, Canada and other parts of Europe, gluten-free foods have become quite popular. With a prevalence rate of about one in 100 - 133 people worldwide, celiac disease is widespread across the globe and life-long consumption of gluten-free food is recommended treatment for this allergy. Apart from celiac-disease patients, gluten-free foods are also consumed by health conscious people for weight management and high protein diet and by the patients for diabetes, autism and food allergies. Apart from gluten-free flour, biscuits, cookies and snacks, product innovations like gluten-free beers are becoming very popular. Big data including online blogs, articles, and reviews have played a major role in increased sales of gluten-free foods. Thus, analysis of editorial and social media content becomes essential to understand the leading trends in gluten-free foods. This study provided deep insights about positive, negative and neutral sentiments related to gluten-free foods using the data from Perspectory Media Insights and Google Trends. This study also revealed that most of the consumers talked and expected product innovation in food sections like snacks, fast food (pizza, pasta and noodles) and desserts through comments on social and editorial media. Searches were divided into developed (e.g., U.S.A.) and developing nations (e.g., India) to get more details about the consumer preferences. This study would help manufacturers of gluten-free foods to develop food products according to the choices and preferences of consumers. The study is very unique in itself since it combines big data to niche food market of gluten-free foods to draw the valuable consumer preferences using online platforms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it